The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A systematic review of the effectiveness of mobile apps for monitoring and management of mental health symptoms or disorders
Abstract
Background There are a growing number of mobile apps available for monitoring and management of mental health symptoms or disorders. However, clinically validated evidence for most of them is unclear; their benefits to patients on long term use are thus debatable.Objective This updated review aimed to systematically appraise the available research evidence of the efficacy and acceptability of mobile apps for mental health in all ages.Methods A comprehensive literature search (May 2013 to December 2017) in PubMed, Cochrane Library, EMBASE, Web of Science, and Google Scholar was conducted. Abstracts were included if they described mental health apps (targeting depression, anxiety, bipolar disorder, psychosis, post-traumatic stress disorders (PTSD), substance use disorders, sleep disorders, and suicidal behaviors) delivered on mobile devices for all ages.Results In total, 1501 abstracts were identified. Of these, 17 publications describing 16 apps targeting anxiety/stress, alcohol disorder, sleep disorder, depression, suicidal behaviors, and PTSD met the inclusion criteria. Five studies randomized individuals to trial conditions, and 14 apps were proven to have clinically validated evidence in reducing mental health symptoms or disorder.Conclusions Mental health apps have potentials in improving the monitoring and management of mental health symptoms or disorders. However, majority of the apps that are currently available lack clinically validated evidence of their efficacy. Given the number and pace at which mobile Health (mHealth) apps are being released, further robust research is warranted to develop and test evidence-based programs.
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“Mental health apps provide a potential way to reduce symptoms and increase adherence by dispensing psychoeducation and other therapeutic skills through an electronic, easily accessible format [26-34].”
From Examining the effectiveness of gamification in mental health apps for depression: Systematic review and meta-analysis (2021) · cites this paper as [34]
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Metadata
- Authors
- Wang, K., Varma, D. S., & Prosperi, M.
- Affiliations
- University of Florida (US)
- Corresponding author
- Prosperi, Mattia
- Venue
- Journal of Psychiatric Research
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 107
- Pages
- 73-78
- ISSN
- 0022-3956 (ISSN-L)
- OpenAlex
- W2894742168
- Cited by
- 308
- References
- 40
- Citations by year
- 2026: 23 · 2025: 29 · 2024: 48 · 2023: 53 · 2022: 61 · 2021: 46 · 2020: 34 · 2019: 11 · 2018: 3
- FWCI
- 3.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Psychiatric Research (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- mental health apps, mobile health, depression, anxiety, bipolar disorder, psychosis, post-traumatic stress disorder, substance use disorders, sleep disorders, suicidal behavior, symptom monitoring, treatment efficacy, acceptability
- MeSH terms
- Mental Disorders, Humans, Outcome Assessment, Health Care, Telemedicine, Monitoring, Ambulatory, Disease Management, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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